Archive for the ‘Metrics & Analytics’ Category
Posted by Nic Winters on December 8th, 2015
Many of our SubscriberMail® users rely upon the Message Summary Report within their account to provide them with actionable information about their past campaigns and help them communicate results with others at their company. However, sometimes we find that the complex URL structure used by some clients can lead to a click report that is less than ideal (especially for those that may not have worked on the email project and are simply looking for quick details).
For example, these URLs may make sense to the person that crafted an email campaign:
… but to others on their team, they may not know what products were included on those pages, etc.
With the Link Label feature within SubscriberMail, you can update these types of links so that they are more informative, such as:
MyWebsite – Home Page
MyWebsite – Specials & Promotions
AnotherWebsite – Featured Product #367
Upon creation, Link Label entries will automatically update relevant links within the Message Summary Report area and continue to apply them to all subsequent messages sent from the SubscriberMail account where they were created — there’s no need to recreate existing ones for future messages.
In addition to Link Labels, our Link Rollup feature allows for the grouping of related links that would normally be listed individually in reports. For instance, consider the following unlabeled links:
Although the three raw URLs above all lead to the same product pages, each one would normally be treated as an individual entry in the Click Through Details portion of the SubscriberMail report. However, you can use the Link Rollups feature to combine such similar links to be displayed like this:
…then create a label for the rolled-up link to make it appear like this:
MyWebsite.com – My Page
Grouping related links via a Link Rollup allows for much easier data comprehension. In the example above, we only are dealing with 3 links, but imagine a scenario where you’re dealing with far more URLs. Using these features, you can see how Link Rollups and Link Labels can make for a much cleaner report.
Please contact our support team at firstname.lastname@example.org and we would love to walk you through the process of creating link labels and rollups within your SubscriberMail account!
Posted by Kavita Jaswal on August 26th, 2015
In today’s digital world, marketers can reach consumers and target prospects through multiple channels. As consumers engage with your products and services via email, social media, your website, etc., each communication brings consumers one step closer to becoming potential leads. But, many marketers struggle with the next step — targeting those consumers with relevant information once their interest on a product or service has turned from interest to intent. Through data management and targeted messaging, organizations can identify, target and communicate with potential leads who are ready to buy.
The amount of information and data that is out there and available to organizations can give insight into more consumer information. Using reporting tools, marketers can take that collected data and turn it into digestible information that can lead to a better understanding of the consumer and where he/she is in the buying process. Once consumers are in a particular segment of the buyer journey, organizations can target their communications directly to those who have the intent to purchase.
Campaign automation coupled with data intelligence allows organizations to effectively communicate with and provide relevant information to consumers who are ready to buy. By creating targeted messages that give leads up-to-date, relevant information on their chosen product or service, organizations can effectively ensure their potential customers are being communicated with appropriate information in real-time.
The emergence of new technologies have given organizations the ability to identify and target potential leads that have indicated more than just an interest in the product or service being offered, they have shown an intent to purchase. Organizations have the ability to collect and analyze data from a variety of sources to discern where the consumer is in his/her buying journey. Using intelligent reporting and automation, companies can put more targeted efforts into intent marketing.
Posted by Kavita Jaswal on July 31st, 2015
One of the biggest topics surrounding financial institutions and digital marketing professionals is data. There are countless articles, blog posts and white papers on data — how to use it, how to understand it and how it can impact the way financial institutions market to potential and current account holders. So what happens with collected data and how can it be used to make the most impact? Online behavior, transactions and social appending are three ways data can be collected and utilized by marketing professionals to improve the account holder experience.
Online behavior gives financial institutions the ability to track purchasing data that shows web browsing habits, consumer trends and insight into audience interests. With the ability to see which sites account holders connect with the most, financial institutions can zero in on common points of interest and send targeted promotions, reducing acquisition costs and allowing more targeted sales efforts.
Capturing real-time transactional information lets financial institutions understand individual account holder preferences, buying patterns and budgeting goals. Understanding what an account holder is purchasing and when they are purchasing it, gives financial institutions insight into account holder needs, providing the opportunity to offer specials or credit card promotions. Transactional information also provides insight into how an account holder is interacting with the financial institution, offering a better understanding of how the relationship is viewed. Transactions also let financial institutions track account holder behavior, enhancing fraud prevention by raising a red flag if an unusual transaction has occurred.
By using data captured from social sites, financial institutions can understand account holders’ perceptions of products and services and, in turn, prevent or reduce account holder churn. By collecting information on how audiences are talking about their specific services, competitors and related topics that influence purchasing behavior, financial institutions can reach a segmented audience that is defined by common attitudes and behaviors. This enables financial institutions to craft targeted messages based on those specifications. Social appending also gives financial institutions a way to identify potential clients from their current account holders’ social media contacts.
Financial institutions can use gathered online behavioral information to find new account holders, offer additional products to current ones and learn how account holders interact with their service offerings. Transactional data gives financial institutions the ability to understand that online behavior in real-time and strategize targeted messaging based on that information. Social appending helps financial institutions understand their account holders on an emotional level and use that information to improve account holder perceptions and gain additional customers. While these are only a few ways data can be collected and used, they can have a significant impact for any financial institution.
Posted by Mallory Green on June 2nd, 2015
We utilize technology in ways that make it unique to our lives. Then, we mold it until it becomes the perfect accessory to our every day activities. If someone is an avid reader, his/her tablet becomes a reading device. If a person is a trail runner, there are countless apps he/she can download to a smartphone to track distance, speed, calories, etc. And the more these apps are used, the more data that is collected based on that person’s interests and behaviors.
But even if all of this information was available in your customer database, would you know what to look for in order to put it to the best use? A Forrester Research study said, that most companies are only looking at 12 percent of their data.1 So the question is… what potential insights are these businesses missing within the 88 percent they aren’t analyzing? Whatever it is, it’s not the data’s fault it wasn’t noticed before. People, not machines, are the ones who analyze and track these interactions.
If I’m a 45-year old female who lives in the suburbs with her three kids, dog and husband, does that mean we can assume all 45-year old females in the same situation think and act like I do? The data might tell you that there are broad similarities, but are we looking close enough to notice the steps that were taken by each individual person that lead to a desired action? If someone left the customer journey early, when, why and where did we lose him/her? How can we use success stories to improve results across the board?
With the ever-changing digital landscape, it’s not about counting, finding the numeric pattern or the value of x, it’s more important to look at the bigger picture and ask the right questions leading to deeper meanings and insights. The numbers don’t create meaning; they just kind of point us all in the right direction. It’s up to those analyzing those numbers to put on their thinking caps and put their critical thinking skills to the test.
The key to understanding and effectively using all that data you have is finding the right formulas that work for your business and ensure there are processes in place to test these formulas regularly. Just because something works for you today, doesn’t mean it will give you the same results one year from now. Marketers and analysts must adapt with technology and trends. The numbers alone won’t give you that “a-ha” moment, but the data along with critical thinking will.
1. Shilmover, Fred. (20, May, 2015). “Forget Big Data-Small Data Is Where The Money Lies.” Datanami. http://www.datanami.com/2015/05/20/forget-big-data-small-data-is-where-the-money-lies/
Posted by Margaret Henry, Ph.D. on April 7th, 2015
One of the most important issues when conducting survey research is to determine the number of returned survey responses necessary to produce valid and reliable results. To this end, we need to consider statistical significance.
To meet these requirements, the accepted percentage for both the confidence level and confidence interval must be determined. The confidence level describes the uncertainty of a sampling method. The generally accepted confidence level utilized in survey research is .95 or 95 percent. Basically, meeting this confidence level indicates that if the study was to be conducted 100 times, the results would fall within the same margins 95 percent of the time.
The confidence interval, also referred to as the margin of error, denotes the range of acceptable error for the data. The most readily accepted margin of error for survey research is five percent, whereby the percentages of data results are known to fall within this margin of error.
If we were to conduct a survey and receive a response sample large enough to meet both the accepted confidence level and confidence interval, we can assume the following:
- If we were to conduct this survey 100 more times, we would produce the same results 95 percent of the time with the true percentage falling within a range of -5 to +5 percent of the identified percentage. This is the acceptable confidence level and interval to statistically consider the collected data both valid and reliable
Once the confidence level and interval are determined, then the required sample size can be determined. The formula to calculate sample size requirements for statistical significance takes into account many factors, and the calculation is neither intuitive nor linear. Typically, the lower the population size, the higher the percentage for the required sample size. For example, a population of 100 individuals would require a sample size of 79 responses. However, at a certain point, the sample size necessary to meet statistical significance in terms of representing the entire population reaches a maximum of 384 (many researcher round the number to 400).
A practical example of the interpretation of confidence level and confidence interval would be if you were to survey a population and receive the appropriate number of responses to meet the 95 percent level and five percent interval requirements then you would be confident that the data was both reliable and valid in understanding the results in the following manner:
A statistically significant number of participants responded the following question:
“How satisfied are you with Product X”?
If 80 percent responded that they were satisfied, then you can be assured that if you were to ask this same question to the required number of individuals in a given population 100 times, the results you would consistently achieve would be that satisfaction would be reported by between 75 percent and 85 percent, for 95 of the administrations.